Overview

Dataset statistics

Number of variables22
Number of observations1180
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory202.9 KiB
Average record size in memory176.1 B

Variable types

DateTime1
Numeric15
Categorical6

Alerts

daily has constant value "469.98117470304"Constant
daily_lower has constant value "469.98117470304"Constant
daily_upper has constant value "469.98117470304"Constant
multiplicative_terms has constant value "0.0"Constant
multiplicative_terms_lower has constant value "0.0"Constant
multiplicative_terms_upper has constant value "0.0"Constant
trend is highly overall correlated with yhat_lower and 10 other fieldsHigh correlation
yhat_lower is highly overall correlated with trend and 10 other fieldsHigh correlation
yhat_upper is highly overall correlated with trend and 10 other fieldsHigh correlation
trend_lower is highly overall correlated with trend and 10 other fieldsHigh correlation
trend_upper is highly overall correlated with trend and 10 other fieldsHigh correlation
additive_terms is highly overall correlated with trend and 10 other fieldsHigh correlation
additive_terms_lower is highly overall correlated with trend and 10 other fieldsHigh correlation
additive_terms_upper is highly overall correlated with trend and 10 other fieldsHigh correlation
weekly is highly overall correlated with weekly_lower and 1 other fieldsHigh correlation
weekly_lower is highly overall correlated with weekly and 1 other fieldsHigh correlation
weekly_upper is highly overall correlated with weekly and 1 other fieldsHigh correlation
yearly is highly overall correlated with trend and 10 other fieldsHigh correlation
yearly_lower is highly overall correlated with trend and 10 other fieldsHigh correlation
yearly_upper is highly overall correlated with trend and 10 other fieldsHigh correlation
yhat is highly overall correlated with trend and 10 other fieldsHigh correlation
ds has unique valuesUnique
trend has unique valuesUnique
yhat_lower has unique valuesUnique
yhat_upper has unique valuesUnique
trend_lower has unique valuesUnique
trend_upper has unique valuesUnique
additive_terms has unique valuesUnique
additive_terms_lower has unique valuesUnique
additive_terms_upper has unique valuesUnique
weekly has unique valuesUnique
weekly_lower has unique valuesUnique
weekly_upper has unique valuesUnique
yearly has unique valuesUnique
yearly_lower has unique valuesUnique
yearly_upper has unique valuesUnique
yhat has unique valuesUnique

Reproduction

Analysis started2022-12-29 07:52:55.853552
Analysis finished2022-12-29 07:53:21.473100
Duration25.62 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

ds
Date

Distinct1180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
Minimum2018-01-01 00:00:00
Maximum2021-03-26 00:00:00
2022-12-29T13:23:21.544114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:21.644642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

trend
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3613.3949
Minimum3042.0482
Maximum4242.1436
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2022-12-29T13:23:21.741257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3042.0482
5-th percentile3085.4486
Q13290.5042
median3606.725
Q33924.8422
95-th percentile4178.6834
Maximum4242.1436
Range1200.0954
Interquartile range (IQR)634.33807

Descriptive statistics

Standard deviation357.73449
Coefficient of variation (CV)0.099002323
Kurtosis-1.2513954
Mean3613.3949
Median Absolute Deviation (MAD)317.43677
Skewness0.066788305
Sum4263806
Variance127973.97
MonotonicityStrictly increasing
2022-12-29T13:23:21.835990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3042.048205 1
 
0.1%
3806.157025 1
 
0.1%
3824.457635 1
 
0.1%
3823.381129 1
 
0.1%
3822.304622 1
 
0.1%
3820.151609 1
 
0.1%
3819.075103 1
 
0.1%
3817.998596 1
 
0.1%
3816.92209 1
 
0.1%
3815.845583 1
 
0.1%
Other values (1170) 1170
99.2%
ValueCountFrequency (%)
3042.048205 1
0.1%
3042.784429 1
0.1%
3043.520654 1
0.1%
3044.256878 1
0.1%
3044.993103 1
0.1%
3045.729327 1
0.1%
3046.465552 1
0.1%
3047.201776 1
0.1%
3047.938001 1
0.1%
3048.674225 1
0.1%
ValueCountFrequency (%)
4242.143647 1
0.1%
4241.067137 1
0.1%
4239.990627 1
0.1%
4238.914117 1
0.1%
4237.837606 1
0.1%
4236.761096 1
0.1%
4235.684586 1
0.1%
4234.608076 1
0.1%
4233.531565 1
0.1%
4232.455055 1
0.1%

yhat_lower
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2431.294
Minimum347.76565
Maximum4362.4681
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2022-12-29T13:23:21.932458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum347.76565
5-th percentile901.89241
Q11393.0952
median2145.0139
Q33565.0274
95-th percentile4126.4615
Maximum4362.4681
Range4014.7025
Interquartile range (IQR)2171.9322

Descriptive statistics

Standard deviation1142.7868
Coefficient of variation (CV)0.47003234
Kurtosis-1.4702149
Mean2431.294
Median Absolute Deviation (MAD)1048.5557
Skewness0.13792162
Sum2868926.9
Variance1305961.7
MonotonicityNot monotonic
2022-12-29T13:23:22.021528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
852.4565846 1
 
0.1%
1865.612 1
 
0.1%
1736.83783 1
 
0.1%
1685.696536 1
 
0.1%
1744.834694 1
 
0.1%
1861.055518 1
 
0.1%
1965.827557 1
 
0.1%
1848.630621 1
 
0.1%
1838.909724 1
 
0.1%
1866.050198 1
 
0.1%
Other values (1170) 1170
99.2%
ValueCountFrequency (%)
347.7656463 1
0.1%
534.3378241 1
0.1%
536.7137322 1
0.1%
551.6926773 1
0.1%
558.7219077 1
0.1%
563.9011188 1
0.1%
566.6940439 1
0.1%
569.0612237 1
0.1%
574.5830276 1
0.1%
580.4007794 1
0.1%
ValueCountFrequency (%)
4362.46814 1
0.1%
4346.749201 1
0.1%
4333.684893 1
0.1%
4330.947802 1
0.1%
4328.064094 1
0.1%
4323.339243 1
0.1%
4313.919639 1
0.1%
4312.148488 1
0.1%
4295.803843 1
0.1%
4294.102869 1
0.1%

yhat_upper
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5604.7984
Minimum3674.5814
Maximum7627.3711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2022-12-29T13:23:22.121716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3674.5814
5-th percentile4055.3558
Q14583.4318
median5331.8819
Q36724.0753
95-th percentile7304.3199
Maximum7627.3711
Range3952.7897
Interquartile range (IQR)2140.6435

Descriptive statistics

Standard deviation1142.6496
Coefficient of variation (CV)0.20386988
Kurtosis-1.4712381
Mean5604.7984
Median Absolute Deviation (MAD)1062.0118
Skewness0.12973974
Sum6613662.1
Variance1305648
MonotonicityNot monotonic
2022-12-29T13:23:22.212490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4087.026417 1
 
0.1%
5056.181952 1
 
0.1%
4853.979656 1
 
0.1%
4886.358373 1
 
0.1%
4904.587984 1
 
0.1%
4936.369606 1
 
0.1%
5076.717791 1
 
0.1%
5005.692739 1
 
0.1%
4955.154435 1
 
0.1%
5146.528963 1
 
0.1%
Other values (1170) 1170
99.2%
ValueCountFrequency (%)
3674.581424 1
0.1%
3682.546552 1
0.1%
3693.438926 1
0.1%
3701.553195 1
0.1%
3712.50314 1
0.1%
3733.084456 1
0.1%
3751.576202 1
0.1%
3761.265893 1
0.1%
3774.670517 1
0.1%
3776.344595 1
0.1%
ValueCountFrequency (%)
7627.371078 1
0.1%
7566.297618 1
0.1%
7540.934571 1
0.1%
7525.480219 1
0.1%
7522.505132 1
0.1%
7497.931468 1
0.1%
7467.809234 1
0.1%
7466.903919 1
0.1%
7462.054179 1
0.1%
7444.585648 1
0.1%

trend_lower
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3613.2734
Minimum3042.0482
Maximum4238.5072
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2022-12-29T13:23:22.309715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3042.0482
5-th percentile3085.4486
Q13290.5042
median3606.725
Q33924.8422
95-th percentile4177.7221
Maximum4238.5072
Range1196.459
Interquartile range (IQR)634.33807

Descriptive statistics

Standard deviation357.53135
Coefficient of variation (CV)0.098949432
Kurtosis-1.2537158
Mean3613.2734
Median Absolute Deviation (MAD)317.43677
Skewness0.06506663
Sum4263662.6
Variance127828.67
MonotonicityStrictly increasing
2022-12-29T13:23:22.404867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3042.048205 1
 
0.1%
3806.157025 1
 
0.1%
3824.457635 1
 
0.1%
3823.381129 1
 
0.1%
3822.304622 1
 
0.1%
3820.151609 1
 
0.1%
3819.075103 1
 
0.1%
3817.998596 1
 
0.1%
3816.92209 1
 
0.1%
3815.845583 1
 
0.1%
Other values (1170) 1170
99.2%
ValueCountFrequency (%)
3042.048205 1
0.1%
3042.784429 1
0.1%
3043.520654 1
0.1%
3044.256878 1
0.1%
3044.993103 1
0.1%
3045.729327 1
0.1%
3046.465552 1
0.1%
3047.201776 1
0.1%
3047.938001 1
0.1%
3048.674225 1
0.1%
ValueCountFrequency (%)
4238.507176 1
0.1%
4237.478169 1
0.1%
4236.48312 1
0.1%
4235.464879 1
0.1%
4234.442751 1
0.1%
4233.411503 1
0.1%
4232.397929 1
0.1%
4231.410266 1
0.1%
4230.422603 1
0.1%
4229.422316 1
0.1%

trend_upper
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3613.5358
Minimum3042.0482
Maximum4246.1044
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2022-12-29T13:23:22.510656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3042.0482
5-th percentile3085.4486
Q13290.5042
median3606.725
Q33924.8422
95-th percentile4179.8043
Maximum4246.1044
Range1204.0562
Interquartile range (IQR)634.33807

Descriptive statistics

Standard deviation357.97053
Coefficient of variation (CV)0.099063783
Kurtosis-1.2486712
Mean3613.5358
Median Absolute Deviation (MAD)317.43677
Skewness0.068795364
Sum4263972.2
Variance128142.9
MonotonicityStrictly increasing
2022-12-29T13:23:22.604504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3042.048205 1
 
0.1%
3806.157025 1
 
0.1%
3824.457635 1
 
0.1%
3823.381129 1
 
0.1%
3822.304622 1
 
0.1%
3820.151609 1
 
0.1%
3819.075103 1
 
0.1%
3817.998596 1
 
0.1%
3816.92209 1
 
0.1%
3815.845583 1
 
0.1%
Other values (1170) 1170
99.2%
ValueCountFrequency (%)
3042.048205 1
0.1%
3042.784429 1
0.1%
3043.520654 1
0.1%
3044.256878 1
0.1%
3044.993103 1
0.1%
3045.729327 1
0.1%
3046.465552 1
0.1%
3047.201776 1
0.1%
3047.938001 1
0.1%
3048.674225 1
0.1%
ValueCountFrequency (%)
4246.104371 1
0.1%
4244.973131 1
0.1%
4243.841271 1
0.1%
4242.709847 1
0.1%
4241.577121 1
0.1%
4240.447017 1
0.1%
4239.316311 1
0.1%
4238.185606 1
0.1%
4237.0549 1
0.1%
4235.923901 1
0.1%

additive_terms
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean405.51719
Minimum-1201.683
Maximum1991.3893
Zeros0
Zeros (%)0.0%
Negative633
Negative (%)53.6%
Memory size9.3 KiB
2022-12-29T13:23:22.700644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1201.683
5-th percentile-989.47504
Q1-560.15806
median-267.65223
Q31655.3747
95-th percentile1836.6523
Maximum1991.3893
Range3193.0723
Interquartile range (IQR)2215.5328

Descriptive statistics

Standard deviation1107.9857
Coefficient of variation (CV)2.7322781
Kurtosis-1.7430283
Mean405.51719
Median Absolute Deviation (MAD)675.48267
Skewness0.19166742
Sum478510.28
Variance1227632.4
MonotonicityNot monotonic
2022-12-29T13:23:22.791863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-639.9686576 1
 
0.1%
-299.0220737 1
 
0.1%
-537.3353696 1
 
0.1%
-485.2310442 1
 
0.1%
-451.7583661 1
 
0.1%
-461.8030934 1
 
0.1%
-380.9323771 1
 
0.1%
-385.3183021 1
 
0.1%
-371.4245823 1
 
0.1%
-324.8222094 1
 
0.1%
Other values (1170) 1170
99.2%
ValueCountFrequency (%)
-1201.683006 1
0.1%
-1193.955376 1
0.1%
-1182.119514 1
0.1%
-1180.124157 1
0.1%
-1178.864498 1
0.1%
-1178.282662 1
0.1%
-1174.095932 1
0.1%
-1170.935808 1
0.1%
-1170.916221 1
0.1%
-1166.011731 1
0.1%
ValueCountFrequency (%)
1991.389251 1
0.1%
1988.147431 1
0.1%
1983.94339 1
0.1%
1977.789728 1
0.1%
1977.052714 1
0.1%
1965.648907 1
0.1%
1961.493693 1
0.1%
1953.564749 1
0.1%
1951.687149 1
0.1%
1950.676704 1
0.1%

additive_terms_lower
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean405.51719
Minimum-1201.683
Maximum1991.3893
Zeros0
Zeros (%)0.0%
Negative633
Negative (%)53.6%
Memory size9.3 KiB
2022-12-29T13:23:22.884401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1201.683
5-th percentile-989.47504
Q1-560.15806
median-267.65223
Q31655.3747
95-th percentile1836.6523
Maximum1991.3893
Range3193.0723
Interquartile range (IQR)2215.5328

Descriptive statistics

Standard deviation1107.9857
Coefficient of variation (CV)2.7322781
Kurtosis-1.7430283
Mean405.51719
Median Absolute Deviation (MAD)675.48267
Skewness0.19166742
Sum478510.28
Variance1227632.4
MonotonicityNot monotonic
2022-12-29T13:23:22.973664image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-639.9686576 1
 
0.1%
-299.0220737 1
 
0.1%
-537.3353696 1
 
0.1%
-485.2310442 1
 
0.1%
-451.7583661 1
 
0.1%
-461.8030934 1
 
0.1%
-380.9323771 1
 
0.1%
-385.3183021 1
 
0.1%
-371.4245823 1
 
0.1%
-324.8222094 1
 
0.1%
Other values (1170) 1170
99.2%
ValueCountFrequency (%)
-1201.683006 1
0.1%
-1193.955376 1
0.1%
-1182.119514 1
0.1%
-1180.124157 1
0.1%
-1178.864498 1
0.1%
-1178.282662 1
0.1%
-1174.095932 1
0.1%
-1170.935808 1
0.1%
-1170.916221 1
0.1%
-1166.011731 1
0.1%
ValueCountFrequency (%)
1991.389251 1
0.1%
1988.147431 1
0.1%
1983.94339 1
0.1%
1977.789728 1
0.1%
1977.052714 1
0.1%
1965.648907 1
0.1%
1961.493693 1
0.1%
1953.564749 1
0.1%
1951.687149 1
0.1%
1950.676704 1
0.1%

additive_terms_upper
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean405.51719
Minimum-1201.683
Maximum1991.3893
Zeros0
Zeros (%)0.0%
Negative633
Negative (%)53.6%
Memory size9.3 KiB
2022-12-29T13:23:23.071201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1201.683
5-th percentile-989.47504
Q1-560.15806
median-267.65223
Q31655.3747
95-th percentile1836.6523
Maximum1991.3893
Range3193.0723
Interquartile range (IQR)2215.5328

Descriptive statistics

Standard deviation1107.9857
Coefficient of variation (CV)2.7322781
Kurtosis-1.7430283
Mean405.51719
Median Absolute Deviation (MAD)675.48267
Skewness0.19166742
Sum478510.28
Variance1227632.4
MonotonicityNot monotonic
2022-12-29T13:23:23.160081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-639.9686576 1
 
0.1%
-299.0220737 1
 
0.1%
-537.3353696 1
 
0.1%
-485.2310442 1
 
0.1%
-451.7583661 1
 
0.1%
-461.8030934 1
 
0.1%
-380.9323771 1
 
0.1%
-385.3183021 1
 
0.1%
-371.4245823 1
 
0.1%
-324.8222094 1
 
0.1%
Other values (1170) 1170
99.2%
ValueCountFrequency (%)
-1201.683006 1
0.1%
-1193.955376 1
0.1%
-1182.119514 1
0.1%
-1180.124157 1
0.1%
-1178.864498 1
0.1%
-1178.282662 1
0.1%
-1174.095932 1
0.1%
-1170.935808 1
0.1%
-1170.916221 1
0.1%
-1166.011731 1
0.1%
ValueCountFrequency (%)
1991.389251 1
0.1%
1988.147431 1
0.1%
1983.94339 1
0.1%
1977.789728 1
0.1%
1977.052714 1
0.1%
1965.648907 1
0.1%
1961.493693 1
0.1%
1953.564749 1
0.1%
1951.687149 1
0.1%
1950.676704 1
0.1%

daily
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
469.98117470304
1180 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters17700
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row469.98117470304
2nd row469.98117470304
3rd row469.98117470304
4th row469.98117470304
5th row469.98117470304

Common Values

ValueCountFrequency (%)
469.98117470304 1180
100.0%

Length

2022-12-29T13:23:23.243000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-29T13:23:23.325660image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
469.98117470304 1180
100.0%

Most occurring characters

ValueCountFrequency (%)
4 3540
20.0%
9 2360
13.3%
1 2360
13.3%
7 2360
13.3%
0 2360
13.3%
6 1180
 
6.7%
. 1180
 
6.7%
8 1180
 
6.7%
3 1180
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16520
93.3%
Other Punctuation 1180
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 3540
21.4%
9 2360
14.3%
1 2360
14.3%
7 2360
14.3%
0 2360
14.3%
6 1180
 
7.1%
8 1180
 
7.1%
3 1180
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 1180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 3540
20.0%
9 2360
13.3%
1 2360
13.3%
7 2360
13.3%
0 2360
13.3%
6 1180
 
6.7%
. 1180
 
6.7%
8 1180
 
6.7%
3 1180
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 3540
20.0%
9 2360
13.3%
1 2360
13.3%
7 2360
13.3%
0 2360
13.3%
6 1180
 
6.7%
. 1180
 
6.7%
8 1180
 
6.7%
3 1180
 
6.7%

daily_lower
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
469.98117470304
1180 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters17700
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row469.98117470304
2nd row469.98117470304
3rd row469.98117470304
4th row469.98117470304
5th row469.98117470304

Common Values

ValueCountFrequency (%)
469.98117470304 1180
100.0%

Length

2022-12-29T13:23:23.381618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-29T13:23:23.445474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
469.98117470304 1180
100.0%

Most occurring characters

ValueCountFrequency (%)
4 3540
20.0%
9 2360
13.3%
1 2360
13.3%
7 2360
13.3%
0 2360
13.3%
6 1180
 
6.7%
. 1180
 
6.7%
8 1180
 
6.7%
3 1180
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16520
93.3%
Other Punctuation 1180
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 3540
21.4%
9 2360
14.3%
1 2360
14.3%
7 2360
14.3%
0 2360
14.3%
6 1180
 
7.1%
8 1180
 
7.1%
3 1180
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 1180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 3540
20.0%
9 2360
13.3%
1 2360
13.3%
7 2360
13.3%
0 2360
13.3%
6 1180
 
6.7%
. 1180
 
6.7%
8 1180
 
6.7%
3 1180
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 3540
20.0%
9 2360
13.3%
1 2360
13.3%
7 2360
13.3%
0 2360
13.3%
6 1180
 
6.7%
. 1180
 
6.7%
8 1180
 
6.7%
3 1180
 
6.7%

daily_upper
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
469.98117470304
1180 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters17700
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row469.98117470304
2nd row469.98117470304
3rd row469.98117470304
4th row469.98117470304
5th row469.98117470304

Common Values

ValueCountFrequency (%)
469.98117470304 1180
100.0%

Length

2022-12-29T13:23:23.512605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-29T13:23:23.577038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
469.98117470304 1180
100.0%

Most occurring characters

ValueCountFrequency (%)
4 3540
20.0%
9 2360
13.3%
1 2360
13.3%
7 2360
13.3%
0 2360
13.3%
6 1180
 
6.7%
. 1180
 
6.7%
8 1180
 
6.7%
3 1180
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16520
93.3%
Other Punctuation 1180
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 3540
21.4%
9 2360
14.3%
1 2360
14.3%
7 2360
14.3%
0 2360
14.3%
6 1180
 
7.1%
8 1180
 
7.1%
3 1180
 
7.1%
Other Punctuation
ValueCountFrequency (%)
. 1180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 17700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 3540
20.0%
9 2360
13.3%
1 2360
13.3%
7 2360
13.3%
0 2360
13.3%
6 1180
 
6.7%
. 1180
 
6.7%
8 1180
 
6.7%
3 1180
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 3540
20.0%
9 2360
13.3%
1 2360
13.3%
7 2360
13.3%
0 2360
13.3%
6 1180
 
6.7%
. 1180
 
6.7%
8 1180
 
6.7%
3 1180
 
6.7%

weekly
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0023642314
Minimum-34.584504
Maximum25.190841
Zeros0
Zeros (%)0.0%
Negative674
Negative (%)57.1%
Memory size9.3 KiB
2022-12-29T13:23:23.643098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-34.584504
5-th percentile-34.584504
Q1-13.990317
median-4.4111598
Q322.469755
95-th percentile25.190841
Maximum25.190841
Range59.775345
Interquartile range (IQR)36.460072

Descriptive statistics

Standard deviation20.556681
Coefficient of variation (CV)8694.8684
Kurtosis-1.2192731
Mean0.0023642314
Median Absolute Deviation (MAD)21.023511
Skewness-0.22804319
Sum2.7897931
Variance422.57713
MonotonicityNot monotonic
2022-12-29T13:23:23.729538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.6123509 1
 
0.1%
-13.99031705 1
 
0.1%
-11.28696606 1
 
0.1%
16.6123509 1
 
0.1%
25.19084103 1
 
0.1%
-34.58450384 1
 
0.1%
22.46975479 1
 
0.1%
-4.411159769 1
 
0.1%
-11.28696606 1
 
0.1%
16.6123509 1
 
0.1%
Other values (1170) 1170
99.2%
ValueCountFrequency (%)
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
ValueCountFrequency (%)
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%

weekly_lower
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0023642314
Minimum-34.584504
Maximum25.190841
Zeros0
Zeros (%)0.0%
Negative674
Negative (%)57.1%
Memory size9.3 KiB
2022-12-29T13:23:23.822378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-34.584504
5-th percentile-34.584504
Q1-13.990317
median-4.4111598
Q322.469755
95-th percentile25.190841
Maximum25.190841
Range59.775345
Interquartile range (IQR)36.460072

Descriptive statistics

Standard deviation20.556681
Coefficient of variation (CV)8694.8684
Kurtosis-1.2192731
Mean0.0023642314
Median Absolute Deviation (MAD)21.023511
Skewness-0.22804319
Sum2.7897931
Variance422.57713
MonotonicityNot monotonic
2022-12-29T13:23:23.909364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.6123509 1
 
0.1%
-13.99031705 1
 
0.1%
-11.28696606 1
 
0.1%
16.6123509 1
 
0.1%
25.19084103 1
 
0.1%
-34.58450384 1
 
0.1%
22.46975479 1
 
0.1%
-4.411159769 1
 
0.1%
-11.28696606 1
 
0.1%
16.6123509 1
 
0.1%
Other values (1170) 1170
99.2%
ValueCountFrequency (%)
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
ValueCountFrequency (%)
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%

weekly_upper
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0023642314
Minimum-34.584504
Maximum25.190841
Zeros0
Zeros (%)0.0%
Negative674
Negative (%)57.1%
Memory size9.3 KiB
2022-12-29T13:23:24.003592image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-34.584504
5-th percentile-34.584504
Q1-13.990317
median-4.4111598
Q322.469755
95-th percentile25.190841
Maximum25.190841
Range59.775345
Interquartile range (IQR)36.460072

Descriptive statistics

Standard deviation20.556681
Coefficient of variation (CV)8694.8684
Kurtosis-1.2192731
Mean0.0023642314
Median Absolute Deviation (MAD)21.023511
Skewness-0.22804319
Sum2.7897931
Variance422.57713
MonotonicityNot monotonic
2022-12-29T13:23:24.090314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.6123509 1
 
0.1%
-13.99031705 1
 
0.1%
-11.28696606 1
 
0.1%
16.6123509 1
 
0.1%
25.19084103 1
 
0.1%
-34.58450384 1
 
0.1%
22.46975479 1
 
0.1%
-4.411159769 1
 
0.1%
-11.28696606 1
 
0.1%
16.6123509 1
 
0.1%
Other values (1170) 1170
99.2%
ValueCountFrequency (%)
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
-34.58450384 1
0.1%
ValueCountFrequency (%)
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%
25.19084103 1
0.1%

yearly
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-64.466351
Minimum-1638.2705
Maximum1496.2172
Zeros0
Zeros (%)0.0%
Negative667
Negative (%)56.5%
Memory size9.3 KiB
2022-12-29T13:23:24.180906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1638.2705
5-th percentile-1455.3619
Q1-1030.5414
median-752.21747
Q31188.3243
95-th percentile1372.7736
Maximum1496.2172
Range3134.4877
Interquartile range (IQR)2218.8657

Descriptive statistics

Standard deviation1107.7968
Coefficient of variation (CV)-17.184109
Kurtosis-1.7442335
Mean-64.466351
Median Absolute Deviation (MAD)657.89873
Skewness0.19176997
Sum-76070.294
Variance1227213.7
MonotonicityNot monotonic
2022-12-29T13:23:24.267026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1126.562183 1
 
0.1%
-755.0129314 1
 
0.1%
-996.0295782 1
 
0.1%
-971.8245698 1
 
0.1%
-946.9303819 1
 
0.1%
-897.1997643 1
 
0.1%
-873.3833066 1
 
0.1%
-850.8883171 1
 
0.1%
-830.1187909 1
 
0.1%
-811.415735 1
 
0.1%
Other values (1170) 1170
99.2%
ValueCountFrequency (%)
-1638.270507 1
0.1%
-1638.154388 1
0.1%
-1638.110372 1
0.1%
-1637.079676 1
0.1%
-1636.976871 1
0.1%
-1636.115014 1
0.1%
-1636.012001 1
0.1%
-1634.861972 1
0.1%
-1634.787855 1
0.1%
-1631.581745 1
0.1%
ValueCountFrequency (%)
1496.217235 1
0.1%
1495.696502 1
0.1%
1495.696292 1
0.1%
1494.675115 1
0.1%
1494.624883 1
0.1%
1492.993194 1
0.1%
1491.196203 1
0.1%
1488.771374 1
0.1%
1487.994734 1
0.1%
1485.912072 1
0.1%

yearly_lower
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-64.466351
Minimum-1638.2705
Maximum1496.2172
Zeros0
Zeros (%)0.0%
Negative667
Negative (%)56.5%
Memory size9.3 KiB
2022-12-29T13:23:24.834416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1638.2705
5-th percentile-1455.3619
Q1-1030.5414
median-752.21747
Q31188.3243
95-th percentile1372.7736
Maximum1496.2172
Range3134.4877
Interquartile range (IQR)2218.8657

Descriptive statistics

Standard deviation1107.7968
Coefficient of variation (CV)-17.184109
Kurtosis-1.7442335
Mean-64.466351
Median Absolute Deviation (MAD)657.89873
Skewness0.19176997
Sum-76070.294
Variance1227213.7
MonotonicityNot monotonic
2022-12-29T13:23:24.923858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1126.562183 1
 
0.1%
-755.0129314 1
 
0.1%
-996.0295782 1
 
0.1%
-971.8245698 1
 
0.1%
-946.9303819 1
 
0.1%
-897.1997643 1
 
0.1%
-873.3833066 1
 
0.1%
-850.8883171 1
 
0.1%
-830.1187909 1
 
0.1%
-811.415735 1
 
0.1%
Other values (1170) 1170
99.2%
ValueCountFrequency (%)
-1638.270507 1
0.1%
-1638.154388 1
0.1%
-1638.110372 1
0.1%
-1637.079676 1
0.1%
-1636.976871 1
0.1%
-1636.115014 1
0.1%
-1636.012001 1
0.1%
-1634.861972 1
0.1%
-1634.787855 1
0.1%
-1631.581745 1
0.1%
ValueCountFrequency (%)
1496.217235 1
0.1%
1495.696502 1
0.1%
1495.696292 1
0.1%
1494.675115 1
0.1%
1494.624883 1
0.1%
1492.993194 1
0.1%
1491.196203 1
0.1%
1488.771374 1
0.1%
1487.994734 1
0.1%
1485.912072 1
0.1%

yearly_upper
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-64.466351
Minimum-1638.2705
Maximum1496.2172
Zeros0
Zeros (%)0.0%
Negative667
Negative (%)56.5%
Memory size9.3 KiB
2022-12-29T13:23:25.016052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1638.2705
5-th percentile-1455.3619
Q1-1030.5414
median-752.21747
Q31188.3243
95-th percentile1372.7736
Maximum1496.2172
Range3134.4877
Interquartile range (IQR)2218.8657

Descriptive statistics

Standard deviation1107.7968
Coefficient of variation (CV)-17.184109
Kurtosis-1.7442335
Mean-64.466351
Median Absolute Deviation (MAD)657.89873
Skewness0.19176997
Sum-76070.294
Variance1227213.7
MonotonicityNot monotonic
2022-12-29T13:23:25.103580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1126.562183 1
 
0.1%
-755.0129314 1
 
0.1%
-996.0295782 1
 
0.1%
-971.8245698 1
 
0.1%
-946.9303819 1
 
0.1%
-897.1997643 1
 
0.1%
-873.3833066 1
 
0.1%
-850.8883171 1
 
0.1%
-830.1187909 1
 
0.1%
-811.415735 1
 
0.1%
Other values (1170) 1170
99.2%
ValueCountFrequency (%)
-1638.270507 1
0.1%
-1638.154388 1
0.1%
-1638.110372 1
0.1%
-1637.079676 1
0.1%
-1636.976871 1
0.1%
-1636.115014 1
0.1%
-1636.012001 1
0.1%
-1634.861972 1
0.1%
-1634.787855 1
0.1%
-1631.581745 1
0.1%
ValueCountFrequency (%)
1496.217235 1
0.1%
1495.696502 1
0.1%
1495.696292 1
0.1%
1494.675115 1
0.1%
1494.624883 1
0.1%
1492.993194 1
0.1%
1491.196203 1
0.1%
1488.771374 1
0.1%
1487.994734 1
0.1%
1485.912072 1
0.1%

multiplicative_terms
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0.0
1180 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3540
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1180
100.0%

Length

2022-12-29T13:23:25.186158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-29T13:23:25.253327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1180
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2360
66.7%
. 1180
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2360
66.7%
Other Punctuation 1180
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2360
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3540
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2360
66.7%
. 1180
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2360
66.7%
. 1180
33.3%

multiplicative_terms_lower
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0.0
1180 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3540
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1180
100.0%

Length

2022-12-29T13:23:25.307148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-29T13:23:25.371129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1180
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2360
66.7%
. 1180
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2360
66.7%
Other Punctuation 1180
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2360
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3540
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2360
66.7%
. 1180
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2360
66.7%
. 1180
33.3%

multiplicative_terms_upper
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.3 KiB
0.0
1180 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3540
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1180
100.0%

Length

2022-12-29T13:23:25.426843image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-29T13:23:25.491108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1180
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2360
66.7%
. 1180
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2360
66.7%
Other Punctuation 1180
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2360
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1180
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3540
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2360
66.7%
. 1180
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2360
66.7%
. 1180
33.3%

yhat
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4018.9121
Minimum2129.8554
Maximum5944.6594
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.3 KiB
2022-12-29T13:23:25.558808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2129.8554
5-th percentile2480.2492
Q12999.1175
median3761.2115
Q35154.4024
95-th percentile5709.8573
Maximum5944.6594
Range3814.804
Interquartile range (IQR)2155.2849

Descriptive statistics

Standard deviation1140.4925
Coefficient of variation (CV)0.28378141
Kurtosis-1.4838897
Mean4018.9121
Median Absolute Deviation (MAD)1057.2499
Skewness0.13389817
Sum4742316.3
Variance1300723.2
MonotonicityNot monotonic
2022-12-29T13:23:25.653098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2402.079547 1
 
0.1%
3507.134952 1
 
0.1%
3287.122266 1
 
0.1%
3338.150084 1
 
0.1%
3370.546256 1
 
0.1%
3358.348516 1
 
0.1%
3438.142726 1
 
0.1%
3432.680294 1
 
0.1%
3445.497508 1
 
0.1%
3491.023374 1
 
0.1%
Other values (1170) 1170
99.2%
ValueCountFrequency (%)
2129.85535 1
0.1%
2142.734761 1
0.1%
2150.491631 1
0.1%
2164.402349 1
0.1%
2173.775615 1
0.1%
2178.218546 1
0.1%
2180.862433 1
0.1%
2191.434294 1
0.1%
2193.949143 1
0.1%
2194.068096 1
0.1%
ValueCountFrequency (%)
5944.659385 1
0.1%
5923.313713 1
0.1%
5921.761145 1
0.1%
5920.500829 1
0.1%
5916.389017 1
0.1%
5904.133943 1
0.1%
5896.609543 1
0.1%
5896.585188 1
0.1%
5893.093028 1
0.1%
5884.391623 1
0.1%

Interactions

2022-12-29T13:23:19.797282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:00.868277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:02.230399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:03.583077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:05.133627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:06.398766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:07.668604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:08.965030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:10.506590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:11.778395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:13.180579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:14.407473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:15.630680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:16.894642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:18.523202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:19.883201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:00.969353image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:02.316613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:03.668953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:05.218872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:06.481371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:07.750958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:09.046268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:10.590228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:11.858521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:13.265784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:14.487103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:15.711797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:16.977129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:18.605803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:19.973547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:01.060329image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:02.409200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:03.759209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:05.309925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:06.571265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:07.837043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:09.133870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:10.680282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:11.942747image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:13.362849image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:14.573538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:15.800274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:17.431020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:18.692561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:20.066112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:01.148044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:02.501963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:03.851706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:05.398820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:06.661151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:07.925345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:09.222059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:10.772606image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:12.029189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:13.449667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:14.660881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:15.886868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:17.517400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:18.788769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:20.150878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:01.231227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:02.591161image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:03.938379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:05.480304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:06.744940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:08.011514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:09.305443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:10.856175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:12.113547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:13.527223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:14.740972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:15.969634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:17.600520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:18.889629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:20.236656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:01.317174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:02.682819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:04.023082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:05.564697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:06.826796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:08.095076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:09.390172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:10.941560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:12.197950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:13.609370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:14.829048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:16.051785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:17.683327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:18.972507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:20.321935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:01.400861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:02.773347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:04.110146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:05.649584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:06.912709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:08.177169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:09.472637image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:11.027192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:12.286492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:13.688225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:14.910129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:16.134446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:17.763800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:19.053980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:20.405936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:01.482684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:02.862861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:04.196040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:05.733330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:06.996624image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:08.283993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:09.556888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:11.111322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:12.403225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:13.770585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:14.990364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:16.216823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:17.846116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:19.137942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:20.495789image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:01.569899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:02.955383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:04.506835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:05.818139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:07.084176image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:08.367265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:09.642116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:11.196360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:12.564702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:13.851086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:15.072817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:16.301108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:17.930587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:19.218578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:20.577477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:01.657288image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:03.039561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:04.590024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:05.899036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:07.166997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:08.447088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:09.723915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:11.276875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:12.695683image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:13.926335image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:15.150616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:16.381723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:18.010598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:19.298661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:20.660465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:01.758941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:03.122402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:04.674551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:05.981858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:07.250671image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:08.527633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:09.804294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:11.361707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:12.778321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:14.002812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:15.228634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:16.469251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:18.090045image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:19.377451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:20.743399image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:01.845778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:03.216130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:04.771205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:06.060922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:07.330794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:08.606217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:09.884427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:11.441378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:12.855746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:14.078638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:15.304281image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:16.561951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:18.170412image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:19.457216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:20.827587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:01.950946image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:03.318954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:04.860773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:06.142778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:07.412812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:08.689792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:10.254896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:11.524246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:12.935690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:14.162517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:15.383759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:16.645880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:18.254265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:19.538919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:20.912577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:02.041684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:03.406183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:04.950791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:06.225676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:07.494817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:08.789534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:10.335654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:11.608184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:13.015922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:14.241935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:15.463650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:16.727426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:18.338731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:19.620290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:20.998240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:02.146791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:03.492816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:05.040416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:06.311694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:07.578955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:08.876381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:10.416923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:11.691164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:13.094891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:14.322689image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:15.543763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:16.808024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:18.433435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-12-29T13:23:19.703085image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-12-29T13:23:25.746839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-29T13:23:25.896074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-29T13:23:26.077146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-29T13:23:26.256056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-29T13:23:26.431177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-29T13:23:21.140441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-29T13:23:21.370479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

dstrendyhat_loweryhat_uppertrend_lowertrend_upperadditive_termsadditive_terms_loweradditive_terms_upperdailydaily_lowerdaily_upperweeklyweekly_lowerweekly_upperyearlyyearly_loweryearly_uppermultiplicative_termsmultiplicative_terms_lowermultiplicative_terms_upperyhat
02018-01-013042.048205852.4565854087.0264173042.0482053042.048205-639.968658-639.968658-639.968658469.981175469.981175469.98117516.61235116.61235116.612351-1126.562183-1126.562183-1126.5621830.00.00.02402.079547
12018-01-023042.784429943.8703524038.4992873042.7844293042.784429-650.109098-650.109098-650.109098469.981175469.981175469.981175-11.286966-11.286966-11.286966-1108.803306-1108.803306-1108.8033060.00.00.02392.675332
22018-01-033043.520654747.8583933927.5975803043.5206543043.520654-624.292184-624.292184-624.292184469.981175469.981175469.981175-4.411160-4.411160-4.411160-1089.862199-1089.862199-1089.8621990.00.00.02419.228470
32018-01-043044.256878863.4605774163.0813813044.2568783044.256878-577.364032-577.364032-577.364032469.981175469.981175469.98117522.46975522.46975522.469755-1069.814962-1069.814962-1069.8149620.00.00.02466.892846
42018-01-053044.993103876.5602433961.4942333044.9931033044.993103-613.393704-613.393704-613.393704469.981175469.981175469.981175-34.584504-34.584504-34.584504-1048.790375-1048.790375-1048.7903750.00.00.02431.599398
52018-01-063045.729327888.7781324144.7792873045.7293273045.729327-570.975093-570.975093-570.975093469.981175469.981175469.981175-13.990317-13.990317-13.990317-1026.965950-1026.965950-1026.9659500.00.00.02474.754235
62018-01-073046.4655521082.0349764165.5022353046.4655523046.465552-509.390226-509.390226-509.390226469.981175469.981175469.98117525.19084125.19084125.190841-1004.562242-1004.562242-1004.5622420.00.00.02537.075325
72018-01-083047.201776901.1345904048.9815013047.2017763047.201776-495.242112-495.242112-495.242112469.981175469.981175469.98117516.61235116.61235116.612351-981.835637-981.835637-981.8356370.00.00.02551.959664
82018-01-093047.9380011053.9064624105.9873183047.9380013047.938001-500.375649-500.375649-500.375649469.981175469.981175469.981175-11.286966-11.286966-11.286966-959.069858-959.069858-959.0698580.00.00.02547.562351
92018-01-103048.674225936.1744554326.0016773048.6742253048.674225-470.996465-470.996465-470.996465469.981175469.981175469.981175-4.411160-4.411160-4.411160-936.566480-936.566480-936.5664800.00.00.02577.677760
dstrendyhat_loweryhat_uppertrend_lowertrend_upperadditive_termsadditive_terms_loweradditive_terms_upperdailydaily_lowerdaily_upperweeklyweekly_lowerweekly_upperyearlyyearly_loweryearly_uppermultiplicative_termsmultiplicative_terms_lowermultiplicative_terms_upperyhat
11702021-03-174232.4550551924.2853535167.8618914229.4223164235.923901-604.048358-604.048358-604.048358469.981175469.981175469.981175-4.411160-4.411160-4.411160-1069.618373-1069.618373-1069.6183730.00.00.03628.406697
11712021-03-184233.5315652133.1788725397.3333634230.4226034237.054900-563.061218-563.061218-563.061218469.981175469.981175469.98117522.46975522.46975522.469755-1055.512148-1055.512148-1055.5121480.00.00.03670.470347
11722021-03-194234.6080762177.6758835321.4204934231.4102664238.185606-605.346440-605.346440-605.346440469.981175469.981175469.981175-34.584504-34.584504-34.584504-1040.743111-1040.743111-1040.7431110.00.00.03629.261635
11732021-03-204235.6845862015.5456705351.4340674232.3979294239.316311-570.005913-570.005913-570.005913469.981175469.981175469.981175-13.990317-13.990317-13.990317-1025.996771-1025.996771-1025.9967710.00.00.03665.678673
11742021-03-214236.7610962006.7624165461.1344984233.4115034240.447017-516.809306-516.809306-516.809306469.981175469.981175469.98117525.19084125.19084125.190841-1011.981322-1011.981322-1011.9813220.00.00.03719.951790
11752021-03-224237.8376061962.8460725426.8631894234.4427514241.577121-512.813075-512.813075-512.813075469.981175469.981175469.98117516.61235116.61235116.612351-999.406600-999.406600-999.4066000.00.00.03725.024532
11762021-03-234238.9141172019.8193635260.5451644235.4648794242.709847-530.267983-530.267983-530.267983469.981175469.981175469.981175-11.286966-11.286966-11.286966-988.962192-988.962192-988.9621920.00.00.03708.646134
11772021-03-244239.9906272155.7295415436.7894574236.4831204243.841271-515.725274-515.725274-515.725274469.981175469.981175469.981175-4.411160-4.411160-4.411160-981.295289-981.295289-981.2952890.00.00.03724.265353
11782021-03-254241.0671372211.1349075268.2146544237.4781694244.973131-484.537991-484.537991-484.537991469.981175469.981175469.98117522.46975522.46975522.469755-976.988921-976.988921-976.9889210.00.00.03756.529146
11792021-03-264242.1436472025.6275265251.2040454238.5071764246.104371-541.144503-541.144503-541.144503469.981175469.981175469.981175-34.584504-34.584504-34.584504-976.541174-976.541174-976.5411740.00.00.03700.999144